How to Analyze a Cost-Effectiveness Dataset

A Gentle Introduction

28 June 2026, 1:30 PM – 4:55 PM | 13:30 – 16:55 (local time)

Faculty

  • Jeffrey S. Hoch, PhD – Professor of Health Policy and Management, University of California Davis, United States

Course Overview

This session is designed for learners who want to discover how to analyze a dataset with three key variables: cost, outcome, and treatment or intervention option in order to study the cost-effectiveness of a new option.

This introductory course focuses on estimating two key cost-effectiveness statistics and characterizing their statistical uncertainty. While the class emphasizes analyzing cost-effectiveness datasets using relatively simple empirical methods, the concepts and interpretation of findings extend to more complex modeling approaches, including decision trees and Markov models.

Participants will learn how to analyze cost-effectiveness data using regression-based approaches, with a particular focus on net benefit regression. The course will also incorporate discussion of how these methods can be taught effectively at different levels, from undergraduate to postdoctoral training.

Learning Objectives

Participants will learn how to:

  • Explain the rationale for person-level (or empirical) cost-effectiveness analysis
  • Define what constitutes a cost-effectiveness dataset
  • Name and distinguish between the two primary cost-effectiveness statistics
  • Describe how net benefit regression can produce estimates of these statistics
  • Describe how net benefit regression can characterize statistical uncertainty
  • Explain how analyses can be extended to more complex scenarios

Course Format

The course will combine short lectures, discussion, and interactive exercises. Participants will work through examples that illustrate estimation and interpretation of cost-effectiveness results using regression-based approaches.

There will be substantial time allocated for discussion and group work to reinforce key concepts and allow participants to engage with the material in an applied context.

Participant Requirements

There are no prerequisites for this introductory course.

Participants who wish to participate in hands-on exercises should bring a laptop (or access to one) with software capable of performing regression analyses (e.g., Excel, Stata, SAS, or R).

Participants interested in teaching this material are encouraged to review the following paper in advance:

Hoch JS et al. Analyzing a Cost-Effectiveness Dataset: A Speech and Language Example for Clinicians (2022).
https://pmc.ncbi.nlm.nih.gov/articles/PMC9300047/

Participants should be prepared to discuss the strengths and limitations of this paper for their intended audience.